System and method for characterizing a system

ABSTRACT

A method and apparatus of identifying a system is disclosed. The method comprises estimating at least one parameter in a second-order model of the system, and approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter. The method may also comprise sampling an input to the system, sampling an output from the system. The apparatus comprises a device for estimating at least one parameter in a second-order model of the system, and device for approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter.

FIELD OF THE INVENTION

[0001] The present invention relates to a system and method for estimating parameters in a first-order plus time delay model using discrete samples of input-output data. More particularly, the present invention relates to a system and method for an automated testing tool that directly estimates the parameters in a second-order continuous-time model from input-output measurements.

BACKGROUND OF THE INVENTION

[0002] The three-parameter model comprising a steady-state gain, time constant, and time delay is well known in the controls industry and may form the basis of numerous tuning methods for PID controllers. The three-parameter model represents an information-efficient means of characterizing HVAC systems and also most chemical engineering processes to a reasonable degree of accuracy, even when the actual order is very high. Although the three-parameter model can be identified in heuristic ways through the application of specific test signals, an automated method of identification is desired due to the existence of multiple minima in parameter estimation cost functions formulated from input and output measurements.

[0003] Because the order of the true system is normally not known a priori, application of any type of recursive estimation scheme to the above class of systems would normally require parallel identification of multiple model candidates. An appropriate order would then need to be determined based on some cost function. In HVAC systems, computational resources on control devices are often limited due to the low-cost nature of the industry. Parallel identification of a potentially large number of models might therefore require computational storage and processing capabilities in excess of what is typically available.

[0004] One approach for identifying time delay models from input-output measurements is to estimate the parameters in several models in parallel, each model having a different time delay. However, this approach is computationally expensive and cumbersome to implement on-line, especially when the range of possible time delay values is not known very accurately beforehand. Techniques for estimating time delays in certain types of discrete-time models have been proposed but these techniques only estimate the time delay to the nearest integer multiple of the sampling period. Moreover, the problem of having multiple minima still exists when trying to estimate the time delay more accurately, i.e., when not an exact integer multiple of the sample period.

[0005] Although discrete models such as the ARMAX model are very popular and easy to implement, these models suffer from a number of deficiencies. For example, any translation from physically meaningful continuous-time system representations requires non-linear transformations, such as matrix exponentiations. The continuous-time parameters then have a non-linearly distributed relationship with the discrete parameters, making it difficult to relate changes in the estimated parameters to an original parameter of interest. In addition, fixed sampling intervals are normally required for ARMAX and other similar discrete models and the selection of a sampling rate is a critical design decision. It is known that the optimum sampling rate for control and for system identification are normally not the same. Hence, certain adaptive control methods based on discrete models suffer from sub-optimal sampling rates in either the control or identification aspect. In addition, anti-aliasing filters and other signal processing elements that are needed for robust estimation of the discrete model form end up becoming part of the estimated process model.

[0006] Accordingly, it would be advantageous to provide a system and method for characterizing a system. It would also be advantageous to adopt a “state variable filter” (SVF) method as a mechanism for directly estimating the parameters in a continuous-time model from input-output measurements. Direct estimation of continuous-time parameters offers several advantages over the discrete-time methods mentioned above. Most notably, the SVF method is less sensitive to sampling rates and is more amenable to the development of techniques for the estimation of a continuous-value time delay. The present invention demonstrates the latter advantage by developing a new transformation for obtaining the parameters in the desired first-order plus time delay characterization from parameters estimated in the continuous-time model. This approach yields near-optimal parameter estimates for given input-output data sets. The overall parameter estimation method is implemented as an automated testing tool that automatically applies step changes to the process under investigation, where the duration of each step is dynamically determined from the estimated parameters. Accordingly, it would be desirable to provide a system and method that allows systems, such as HVAC systems, to be automatically tested to validate performance, tune control loops, and configure controllers.

[0007] It would be desirable to provide for a system and method for an automated testing tool having one or more of these or other advantageous features.

SUMMARY OF THE INVENTION

[0008] The present invention relates to a method of identifying a system. The method comprises estimating at least one parameter in a second-order model of the system, and approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter.

[0009] The present invention also relates to a method of analyzing a system to estimate at least one parameter in a first-order model of the system. The method comprises sampling an input to the system, sampling an output from the system, estimating at least one parameter in a second-order model of the system using the sampled inputs and outputs, and approximating the at least one first-order model parameter based on the at least one second-order model parameter.

[0010] The present invention further relates to a method of analyzing a non-first order system having inputs and outputs. The method comprises sampling the inputs, sampling the outputs, and estimating at least one parameter for a first-order model of the system based on the sampled inputs and outputs.

[0011] The present invention further relates to an apparatus for identifying a system. The apparatus comprises means for estimating at least one parameter in a second-order model of the system, and means for approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter.

[0012] The present invention further relates to various features and combinations of features shown and described in the disclosed embodiments.

DESCRIPTION OF THE FIGURES

[0013]FIG. 1 is a graphical representation block diagram of an automated testing tool according to a preferred embodiment.

[0014]FIG. 2 is a block diagram of the automated testing tool.

[0015]FIG. 3 is a graphical representation of linearoptimized valves according to an exemplary embodiment.

[0016]FIG. 4 is a graphical representation of linearfit to estimates from optimization according to an exemplary embodiment.

DETAILED DESCRIPTION OF PREFERRED AND OTHER EXEMPLARY EMBODIMENTS

[0017] According to an exemplary embodiment, an algorithm is used for estimating the parameters in a first-order plus time delay model using discrete samples of input-output data obtained from a system of interest. The algorithm comprises an indirect estimation approach whereby the parameters in a second-order continuous-time model are first estimated through the use of state variable filters. A transformation is then applied to the continuous-time parameters to obtain near-optimal estimates of the desired first-order plus dead-time parameters. High-pass filtering and instrumental variables may also be used to capture behavior within an appropriate frequency range and reduce the impact of unmeasured disturbances such as noise. Parameters may be estimated recursively, thus making the algorithm computationally undemanding and suitable for online usage in low-cost digital micro-controllers.

[0018] According to a preferred embodiment, the algorithm is implemented as an apparatus (e.g., device, system, sub-system, etc.) shown as an automatic testing tool 10 that generates its own test signals in the form of step changes to excite the system or process being characterized. According to a particularly preferred embodiment, the testing tool 10 is adapted for automating the configuration of controllers in heating, ventilating, and air-conditioning (HVAC) installations and also for verifying the performance of the controlled systems. The controllers used with HVAC systems generally require attributes to be set for proper and efficient operation, such as the sign of the process gain, sampling interval, and initial PID tuning parameters. These attributes can be determined from the parameters in the first-order plus time delay model estimated by the testing tool 10. In addition, HVAC systems may be installed incorrectly and/or insufficient time may be available for performing proper commissioning and performance verification due to the manual nature of current practice. The testing tool 10 is configured to facilitate unsupervised “self-testing” of controllers and controlled subsystems by comparing identified parameters with expectations there of during correct operation.

[0019]FIG. 1 shows components that comprise the testing tool 10 according to an exemplary embodiment. A test signal generator 12 applies step changes to an input of a process 14, and measurements of the applied input and measured output are passed to a parameter estimator 16. The parameters in a first-order plus time delay model are then estimated by estimator 16 and fed back to the test signal generator 12, where they are used as a basis for determining when to terminate the test or apply a new step change to the process 14 input. The tool 10 uses an initial estimate of the dominant time constant of the process 14 (T₀) to configure it for initial operation. As discussed further below, the accuracy of this initial estimate is not critical. According to a preferred embodiment, the initial value need only be greater than the actual value and can be several orders of magnitude greater without hampering the ability of the tool 10 to identify the system.

[0020] The tool 10 estimates the following parameters in a first-order plus time delay characterization: a time delay (L) estimated as a continuous value; a time constant (T) in the first-order component of the model; and a static gain (K) estimated in units dependent on the units of the measured input and output signals.

[0021] According to a preferred embodiment, the testing tool 10 is implemented in a digital controller and data are sampled discretely. The three parameters identified above are calculated at each time step in the testing process. Estimates from the tests can be used to pre-tune and configure feedback controllers, such as a proportional-integral-derivative (PID) controller, and also serve as a basis for performance validation. (According to alternative embodiments, any of a variety of controllers may be used.) The latter application can be realized by comparing estimated parameter values with expected values for the type of system being tested. Depending on the accuracy of the expected (correct operation) values, different types of faults could be detected and diagnosed.

[0022] One component of the testing tool 10 is a parameter estimation model. The model should be flexible enough to allow characterization of all possible systems within the general class of interest, but it should be simple enough to facilitate the use of robust estimation methods. In HVAC systems, for example, dynamic behavior in open loop is often the result of many elemental first-order sub-processes and components that serially affect the behavior of the overall process. An appropriate transfer function description for these sorts of systems is given by: $\begin{matrix} {{G(s)} = \frac{K}{\prod\limits_{i = 1}^{N}\quad \left( {1 + {\tau_{i}s}} \right)}} & (1) \end{matrix}$

[0023] where N is the order of the process and all roots of the characteristic equation are negative real and τ₁>τ₂>τ₃> . . . τ_(N). The static gain is shown as a constant K, but may be a non-linear function of inputs, outputs, and time.

[0024] A model that can approximate the class of systems represented by Equation-1 is a first-order plus time delay characterization as given by: $\begin{matrix} {{G(s)} = \frac{K\quad {\exp \left( {- {Ls}} \right)}}{\left( {1 + {Ts}} \right)}} & (2) \end{matrix}$

[0025] where L is a time delay and T is a time constant. Since the exponential term expands to an infinite dimension in the s-operator, this model can be used to approximate a large range of systems of arbitrary order. Because of this flexibility, the three-parameter model described above is an exemplary way of characterizing the dynamics of a system in an information-efficient way. However, one problem with the model is that the incorporation of a time delay causes there to be multiple minima in the cost function that is needed to facilitate estimation of parameter values from input and output measurements.

[0026] To avoid the problem of local minima, an indirect method of identifying the desired first-order plus time delay model. The method involves first estimating the parameters in a second-order transfer function model as given by: $\begin{matrix} {{G(s)} = \frac{b_{1}}{s^{2} + {a_{1}s} + a_{2}}} & (3) \end{matrix}$

[0027] The parameters in the second-order model above are estimated in a least-squares sense from time-series input-output measurements. A transformation is then performed on the second-order model parameters to produce estimates of the parameters in the first-order plus time delay model.

[0028]FIG. 2 is a block diagram showing the main functional components in the algorithm used by an automated testing tool 18. Preferably the testing tool algorithm is implemented in a digital controller and thus constituent components operate in discrete-time where k denotes a sample number. A process 36 under investigation (e.g., being analyzed) has a continuous-time output that is sampled at discrete intervals before being processed by the algorithm. The parameter estimation is preferably made using state variable filters (SVF) 20. In addition, instrumental variables may be used in the estimation to eliminate noise-induced bias in the parameter estimates. The output of an auxiliary model 22 is passed through the SVFs 20 and the instrumental variable vector is constructed using the filtered model output. The instrumental variables and the auxiliary model 22 are described further below. The process 36 being investigated is excited by an input signal u_(k) and produces an output signal x(t) that is corrupted by some additive signal ξ(t). The tool 18 is configured to estimate a first-order plus time delay model from the input signal and the noise-corrupted and discretely sampled output signal (y_(k)). The SVF method provides direct estimation of the parameters in a second-order transfer function by means of a transformation to a “filter operator” form, as described further below. Estimation of the transfer function parameters is preferably made using the recursive least squares (RLS) method (block 38).

[0029] High-pass filtering 24 is configured to eliminate DC-bias in the measured output signal and also focus the estimation on the frequency range of interest. A high-pass filter 26 is also applied to the output from the auxiliary model 22. (Preferably, filter 24 is substantially equivalent to filter 26.) The parameters estimated in the second-order model are passed through a transformation 28 to obtain estimates for time delay (L) and time constant (T) in a first-order plus time delay characterization. The viability of the parameter estimates are checked at block 30, after which the estimated time delay and time constant parameters are passed through a low-pass filter 29. The filtered estimates are then used as the time constants in the auxiliary model 22.

[0030] A convergence test 32 is made on the estimated parameters and upon satisfying the required criteria, the test signal generator 34 uses the parameter estimates to determine when to terminate the test or apply a new step change to the input of process 36. Signal generator 34 preferably initiates a step change when a time period has elapsed since the last step change that is greater than five times the sum of the estimated time delay and time constant (i.e., the estimated average residence time). Assuming parameter estimates are correct, this approach causes the system to be in a near steady-state condition before each new step change is applied. The parameters estimated for each step change are stored for later analysis. Each of the blocks in FIG. 2 is described in more detail in the following sections.

[0031] According to an exemplary embodiment, the second-order transfer function model is transformed into a form that enables the parameters values to be estimated using linear methods such as recursive least squares (RLS).

[0032] Ordinarily, estimation of continuous-time model parameters would require measurements of the system state vector or direct differentiation of the input and output signals. A system state vector is not normally measurable and any noise in measured signals tends to seriously disrupt attainable accuracy of any differentiation procedure.

[0033] The state variable filter method of FIG. 2 avoids the need to obtain measurement of state vectors or differentiated signals by performing a transformation on the transfer function model of interest so that it is written in terms of filtered signals available through the SVF 20. The SVF 20 is comprised of multiple first-order low-pass filters where each of the filters has the following transfer function: $\begin{matrix} {{H(s)} = \frac{1}{1 + {\tau \quad s}}} & (4) \end{matrix}$

[0034] Re-arranging for the s operator: $\begin{matrix} {s = {\frac{1}{\tau}\left( {\frac{1}{H(s)} - 1} \right)}} & (5) \end{matrix}$

[0035] Recall that the second-order transfer function equation was given by: $\begin{matrix} {{G(s)} = {\frac{Y(s)}{U(s)} = \frac{b_{1}}{s^{2} + {a_{1}s} + a_{2}}}} & (6) \end{matrix}$

[0036] Substituting the expression for s: $\begin{matrix} \begin{matrix} {\frac{Y(s)}{U(s)} = \frac{b_{1}\tau^{2}{H(s)}{H(s)}}{1 + {{H(s)}\left( {{a_{1}\tau} - 2} \right)} + {{H(s)}{H(s)}\left( {1 - {a_{1}\tau} + {a_{2}\tau^{2}}} \right)}}} \\ {= \frac{\beta \quad {H(s)}{H(s)}}{1 + {\alpha_{1}{H(s)}} + {\alpha_{2}{H(s)}{H(s)}}}} \end{matrix} & (7) \end{matrix}$

[0037] Hence, in the Laplace domain, the output Y(s) is given by:

Y(s)=−α₁ [Y(s)H(s)]−α₁ [Y(s)H(s)H(s)]+β₁ [U(s)H(s)H(s)]  (8)

[0038] A transformation into the time domain yields the following input-output model:

y(t)=−α₁ y ^(f1)(t)−α₁ y ^(f2)(t)+β₁ u ^(f2)(t)  (9)

[0039] where $\begin{matrix} {{{u^{fN}(t)} = {{L^{- 1}\left( {{U(s)}{\prod\limits_{i = 1}^{N}\quad {H(s)}}} \right)}\quad {and}\quad {likewise},}}\quad} & (10) \\ {{y^{fN}(t)} = {L^{- 1}\left( {{Y(s)}{\prod\limits_{i = 1}^{N}\quad {H(s)}}} \right)}} & (11) \end{matrix}$

[0040] where L⁻¹ is the inverse Laplace transform. Hence, an input-output model has been formulated with parameters that are simple functions of the original transfer function parameters. In addition, the u^(fn) and y^(fn) elements in the model are directly realizable by simply passing the measured input and output signals through the serially-connected bank of first-order filters that comprise the SVF 20 in FIG. 2. Note that y^(fn) refers to the output of the last in a series of n first-order filters where the input to the first filter in the series is the raw signal y(t). For convenience, a “filter operator” λ can be defined, such that:

y(t)λ^(n) =y ^(fn)(t)  (12)

[0041] The above transformation can be applied to any arbitrary transfer function equation. In exemplary embodiments, this model form serves as a continuous-time analog to the discrete model form where a polynomial is defined in the shift operator, such as in ARMAX models. For example, the equation above could be expressed as:

A*(λ)y(t)=B*(λ)u(t)  (13)

[0042] where the “shift” operator is now a “filter” operator:

A*(λ)=1+α₁λ+α₂λ²+ . . . +α_(n)λ^(n)  (14)

B*(λ)=β_(1λ+β) ₂λ²+ . . . +β_(N)λ^(n)  (15)

[0043] Although the input-output model in Equation 9 was expressed in terms of continuous time, the model can also be expressed discretely as follows: $\begin{matrix} {y_{k} = {{{- \alpha_{1}}y_{k}^{f1}} - {\alpha_{1}y_{k}^{f2}} + {\beta_{1}u_{k}^{f2}}}} & (16) \end{matrix}$

[0044] where the subscript k denotes sample number and the signals are discrete samples of their continuous form counterparts. Proceeding with the discrete-time form of the input-output model in the equation above, it is useful for parameter estimation purposes to express the model in the following more familiar form: $\begin{matrix} {{y_{k} = {\theta_{\tau}^{T}\phi_{k}}}{where}{\theta_{\tau}^{T} = \left\lbrack {{- \alpha_{1}} - {\alpha_{2\quad}\beta_{1}}} \right\rbrack}} & (17) \end{matrix}$

[0045] is the parameter vector and ϕ_(k)^(T) = [y_(k)^(f₁)y_(k)^(f₂)u_(k)^(f₂)]

[0046] is the regressor vector. In the more general case for an nth order transfer function: $\begin{matrix} {\theta_{\tau}^{T} = \begin{bmatrix} {- \alpha_{1}} & \ldots & {- \alpha_{n}} & \beta_{1} & \ldots & \beta_{n} \end{bmatrix}} & (18) \\ {\phi_{k}^{T} = \begin{bmatrix} y_{k}^{f_{1}} & \ldots & y_{k}^{f_{n}} & u_{k}^{f_{1}} & \ldots & u_{k}^{f_{n}} \end{bmatrix}} & (19) \end{matrix}$

[0047] There is a linear transformation between the parameters α₁ . . . α_(n), β₁ . . . β_(n) and the original transfer function parameters a₁ . . . a_(n), b₁ . . . b_(n) when the SVF time constant is greater than zero. In general terms, this transformation is given by:

θ_(τ) =Fθ+G  (20)

[0048] where θ^(T)=[−α₁ . . . −α_(n) b₁ . . . b_(n)] is the parameter vector containing the transfer function parameters. F and G are given by: $\begin{matrix} {F = \begin{bmatrix} M & 0 \\ 0 & M \end{bmatrix}} & (21) \\ {with} & \quad \\ {M = {{\begin{bmatrix} m_{11} & 0 & 0 \\ \vdots & ⋰ & 0 \\ m_{n1} & \ldots & m_{n\quad n} \end{bmatrix}\text{;}\quad m_{ij}} = {\left( {- 1} \right)^{i - j}\begin{pmatrix} {n - j} \\ {i - j} \end{pmatrix}\tau^{j}}}} & (22) \\ {and} & \quad \\ {G = {{\begin{bmatrix} g_{1} & \ldots & g_{n} & 0 & \ldots & 0 \end{bmatrix}\text{;}\quad g_{i}} = {\begin{pmatrix} n \\ i \end{pmatrix}\left( {- 1} \right)^{i}}}} & (23) \end{matrix}$

[0049] The matrix F is invertible when M is invertible, which occurs for all τ>0. Since a linear transformation exists between the original transfer function parameters and the parameters in the filtered model formulation, a least-squares approach can be adopted to estimate the transfer function parameters directly from measured input-output data. As such, an output prediction ŷ_(k) can be made directly from the transfer function parameter vector according to: $\begin{matrix} {{{\hat{y}}_{k} = {{\theta_{\tau}^{T}\phi_{k}} = {\left\lbrack {{F\quad \theta} + G} \right\rbrack^{T}\phi_{k}}}}{{\hat{y}}_{k} = {{\theta^{T}\left\lbrack {{F\quad}^{T}\phi_{k}} \right\rbrack} + {G^{T}\phi_{k}}}}} & (24) \end{matrix}$

[0050] For a given set of measured inputs and outputs, a least squares estimate of the parameter vector can thus be found as follows: $\begin{matrix} {{{\hat{y}}_{k} = {{\theta^{T}\left\lbrack {{F\quad}^{T}\phi_{k}} \right\rbrack} + {G^{T}\phi_{k}} + e_{k}}}{S = {{\frac{1}{2}{\sum\limits_{i = 1}^{N}\quad e_{i}^{2}}} = {\frac{1}{2}{\sum\limits_{i = 1}^{N}\left( {y_{i} - {\theta^{T}\left\lbrack {F^{T}\phi_{i}} \right\rbrack} - {G^{T}\phi_{i}}} \right)^{2}}}}}} & (25) \\ {\frac{\partial S}{\partial\theta} = {\sum\limits_{i = 1}^{N}{{- \left\lbrack {F^{T}\phi_{i}} \right\rbrack}\left( {y_{i} - {\theta^{T}\left\lbrack {F^{T}\phi_{i}} \right\rbrack} - {G^{T}\phi_{i}}} \right)}}} & (26) \end{matrix}$

[0051] Hence, a least squares estimate of the parameter vector is obtained when ${\frac{\partial S}{\partial\theta} = 0},$

[0052] thus: $\begin{matrix} {{\hat{\theta}}^{T} = {{\left\{ {\sum\limits_{i = 1}^{N}{\psi_{i}\psi_{i}^{T}}} \right\}^{- 1}\left\{ {\sum\limits_{i = 1}^{N}{\psi_{i}ɛ_{i}}} \right\}} = {P_{N}\left\{ {\sum\limits_{i = 1}^{N}{\psi_{i}ɛ_{i}}} \right\}}}} & (27) \end{matrix}$

[0053] where:

ψ_(i) =F ^(T)φ_(i);ε_(i) =y _(i) −G ^(T)φ_(i)  (28)

[0054] Equation 27 can be written in a recursive form to yield the following familiar set of recursive least-squares (RLS) equations: $\begin{matrix} \begin{matrix} {{\hat{\theta}}_{k} = {{\hat{\theta}}_{k - 1} + {P_{k}{\psi_{k}\left( {ɛ_{k} - {\psi_{k}{\hat{\theta}}_{k - 1}}} \right)}}}} \\ {= {{\hat{\theta}}_{k - 1} + {P_{k}{\psi_{k}\left( {y_{k} - \left\lbrack {{{\hat{\theta}}_{k - 1}^{T}\left\lbrack {F^{T}\phi_{k}} \right\rbrack} + {G^{T}\phi_{k}}} \right\rbrack} \right)}}}} \\ {= {{\hat{\theta}}_{k - 1} + {P_{k}\psi_{k}e_{k}}}} \end{matrix} & (29) \end{matrix}$

[0055] where e_(k)=y_(k)−ŷ_(k) is the prediction error at the k^(th) sample. The update formula for the P matrix is given by: $\begin{matrix} {P_{k} = {P_{k - 1} - \frac{P_{k - 1}\psi_{k}\psi_{k}^{T}P_{k - 1}}{1 + {\psi_{k}^{T}P_{k - 1}\psi_{k}}}}} & (30) \end{matrix}$

[0056] The preceding equations thus provide a way to recursively update the parameters of a continuous-time transfer function equation directly from measurements of inputs and outputs sampled at discrete points in time. As discussed below, the equations may be refined to eliminate, reduce, or minimize bias in the parameter estimated stemming from noise in the measured output signal.

[0057] When there is noise in the measured output signal, this noise may be correlated in some way with the regressor vector (φ). In this situation, the parameter estimates obtained through application of the basic form of the recursive least squares method detailed above will be biased to a degree dependent on the signal-to-noise ratio. Noise and other disturbances that have a zero mean over the long term can be “correlated out” of the estimation using known instrumental variable methods. Recall that the θ value that minimizes the least-squares cost function is the solution of:

Eφ _(k) [y _(k)−φ_(k)θ]=0  (31)

[0058] The instrumental variable method involves replacing what is the gradient vector in the above expression to yield the following:

Eη _(k) [y _(k)−φ_(k)θ]=0  (32)

[0059] where η_(k) is selected to be uncorrelated with the noise term (i.e., ξ(t) in FIG. 2), but strongly correlated with a gradient vector derived from the noise-free output of the system, i.e., x(t) in FIG. 2. Furthermore, the following should hold true:

Eη _(k)ξ_(k)=0  (33)

[0060] Eη_(k)φ_(k) be positive definite, or at least non-singular.

[0061] Introduction of an instrumental variable vector as an approximation to the actual gradient vector allows the least squares equations to be expressed as follows: $\begin{matrix} {{\hat{\theta}}^{T} = {\left\{ {\sum\limits_{i = 1}^{N}{\eta_{i}\psi_{i}^{T}}} \right\}^{- 1}\left\{ {\sum\limits_{i = 1}^{N}{\eta_{i}ɛ_{i}}} \right\}}} & (34) \end{matrix}$

[0062] The recursive form of the least squares equations with the incorporation of the instrumental variable vector is then given by: $\begin{matrix} {{P_{k} = {P_{k - 1} - \frac{P_{k - 1}\eta_{k}\psi_{k}^{T}P_{k - 1}}{1 + {\psi_{k}^{T}P_{k - 1}\eta_{k}}}}}{{\hat{\theta}}_{k} = {{\hat{\theta}}_{k - 1} + {P_{k}\eta_{k}e_{k}}}}} & (35) \end{matrix}$

[0063] According to an exemplary embodiment, it is desirable that η_(k) be derived from a prediction of the noise-free output of the process, i.e., {circumflex over (x)}_(k) in FIG. 2. One way to obtain predictions of {circumflex over (x)}_(k) is to use a model of the process that is driven by the same input. The numerator in the transfer function representation of the auxiliary model is irrelevant both from an accuracy and consistency point of view. Also, taking the denominator of the auxiliary model transfer function to be equal to that of the real system is only optimal when ξ_(k) is in fact white noise. However, without a detailed knowledge of auto-correlation in the noise component, the denominator of the auxiliary model may be set to approximate that of the real system, which is the approach adopted in the preferred embodiment.

[0064] According to an exemplary embodiment, an auxiliary model structured to be of the same order as the estimated second-order model is used to generate the vector of instrumental variables. The auxiliary model thus has two time constants associated with it (τ₁ and τ₂). The numerator of model transfer function is taken to be unity since this part of the transfer function has no impact on the estimation, as was described above. Because the parameter estimation scheme is designed for implementation in a digital controller, the auxiliary model equations are solved discretely. For this purpose a discrete state-space formulation is used so that:

{circumflex over (x)} _(k+1) =C{circumflex over (x)} _(k) +du _(k) ; {circumflex over (x)}=[{circumflex over (x)} ₁ {circumflex over (x)} ₂]  (36)

[0065] where $\begin{matrix} \begin{matrix} {{C = \begin{bmatrix} \rho_{1} & 0 \\ \left\{ {\rho_{1}\left( {1 - \rho_{2}} \right)} \right\} & \rho_{2} \end{bmatrix}};} & {d = \begin{bmatrix} \left( {1 - \rho_{1}} \right) & \left\{ {\left( {1 - \rho_{1}} \right)\left( {1 - \rho_{2}} \right)} \right\} \end{bmatrix}} \end{matrix} & (37) \end{matrix}$

[0066] where ρ₁=exp (−Δt/τ₁) and ρ₂=exp (−Δt/τ₂). During the estimation, the time constants (τ₁ and τ₂) in the auxiliary model are set equal to the estimated time delay (L) and time constant (T), respectively. As such, the roots in the auxiliary model's characteristic equation will be negative and real, which is consistent with the expected form of the system being characterized. Also, a second-order model tends to provide an approximation to a first-order plus time delay process when the time constants are set equal to the desired time delay and first-order time constant. The auxiliary model output is x₂ and this output is passed through the state variable filters in order to generate the following instrumental variable vector: $\begin{matrix} {\eta_{k}^{T} = \begin{bmatrix} {\hat{x}}_{2,k}^{f1} & {\hat{x}}_{2,k}^{f2} & u_{k}^{f2} \end{bmatrix}} & (38) \end{matrix}$

[0067] The above vector is therefore analogous to the regressor vector, but is constructed from the output of the auxiliary model instead of the noise-corrupted measured output.

[0068] The measured output signal is passed through a high-pass filter 24 before being used in the parameter estimation to block out DC bias and eliminate extraneous disturbances that are outside the frequency range of process 36. Recall that the time constant (τ) used in the state variable filters 20 is set to represent an estimate of maximum likely time constant in the process. A high-pass filter with an effective cut-off frequency may therefore be derived from the time constant, τ. According to an exemplary embodiment, a first-order high-pass filter is applied to the measured output signal with a time constant set equal to τ, i.e., $\begin{matrix} {{G_{f}(s)} = \frac{\tau \quad s}{1 + {\tau \quad s}}} & (39) \end{matrix}$

[0069] Because the post-filtered signal is used in the parameter estimation, the parameter estimator 38 will “see” the product of the real process and filter transfer functions, i.e., $\begin{matrix} {\frac{Y^{h\quad p}(s)}{U(s)} = {{G_{p}(s)}{G_{f}(s)}}} & (40) \end{matrix}$

[0070] where G_(p)(s) is the transfer function of the process being identified and the superscript hp denotes the high-pass filtered version of the measured output signal. Direct use of the filtered output signal in the estimation will thus cause the filter transfer function to become part of the estimated process model. However, this may be avoided by first expressing G_(f)(s) in terms of the low-pass filter transfer function H(s) via substitution of the expression for the s operator that was given in Equation 5. Because the high-pass filter and SVF filters 20 use the same time constant τ, the high-pass filter transfer function simplifies to:

G _(f) {H(s)}=1−H(s)  (41)

[0071] The transfer function of the process 36 is then isolated from the estimation by applying the above expression as a multiplier in the s domain to the measured input signal used in the estimation: $\begin{matrix} {\frac{Y^{h\quad p}(s)}{{U(s)}\left\{ {1 - {H(s)}} \right\}} = {G_{p}(s)}} & (42) \end{matrix}$

[0072] Recall that the original regressor vector used in the estimation of the considered second-order model was given by: $\begin{matrix} {\phi_{k}^{T} = \begin{bmatrix} y_{k}^{f1} & y_{k}^{f2} & u_{k}^{f2} \end{bmatrix}} & (43) \end{matrix}$

[0073] Utilization of Equation 42 thus involves simply multiplying the input signals in the regressor vector by G_(f){H(s)}. Hence, since H(s) is a linear operator, the regressor vector can be re-defined as: $\begin{matrix} {\phi_{k}^{T} = \left\lfloor \begin{matrix} \left( y^{h\quad p} \right)_{k}^{f1} & \left( h^{h\quad p} \right)_{k}^{f2} & \left\{ {u_{k}^{f2} - u_{k}^{f3}} \right\} \end{matrix} \right\rfloor} & (44) \end{matrix}$

[0074] The high-pass filtering 24 implemented on the output signal is thus blocked out of the parameter estimation by substituting this new regressor vector in the equations for updating the P matrix and parameter vector. The same transformation is also performed on the vector of instrumental variables so that: $\begin{matrix} {\eta_{k}^{T} = \left\lfloor \begin{matrix} \left( {\hat{x}}_{2}^{h\quad p} \right)_{k}^{f1} & \left( {\hat{x}}_{2}^{h\quad p} \right)_{k}^{f2} & \left\{ {u_{k}^{f2} - u_{k}^{f3}} \right\} \end{matrix} \right\rfloor} & (45) \end{matrix}$

[0075] where the superscript hp denotes the high-pass filtered version of the signals.

[0076] A discrete implementation of the above high-pass filter 24 can be realized as follows: $\begin{matrix} {y_{k}^{h\quad p} = {{\gamma \left( y_{k - 1}^{h\quad p} \right)} + {\gamma \left\lbrack {y_{k} - y_{k - 1}} \right\rbrack}}} & (46) \end{matrix}$

[0077] where γ=exp(−Δt/τ) is the filter constant obtained from the analytical solution of the filter transfer function. The high-pass filters 24, 26 are preferably applied to the measured output and to the auxiliary model output (respectfully).

[0078] As with the auxiliary model, the multiple first-order low-pass filters in the SVF 20 of the testing tool 18 are solved discretely to allow implementation in a digital controller. The discrete state-space model form is used again to filter the measured input (u), the high-pass filtered version of the measured output (y^(hp)), and the high-pass filtered version of the auxiliary model output (x̂₂^(h  p))

[0079] in the following manner: $\begin{matrix} {{u_{k + 1}^{f} + {Au}_{k}^{f} + {Bu}_{k}}{y_{k + 1}^{f} = {{Ay}_{k}^{f} + {By}_{k}^{h\quad p}}}{{\hat{x}}_{k + 1}^{f} = {{A{\hat{x}}_{k}^{f}} + {B{\hat{x}}_{2,k}^{h\quad p}}}}} & (47) \end{matrix}$

[0080] where u^(f)=[u^(f1) . . . u^(fn)]; y^(f)=[y^(f1) . . . y^(fn)]; {circumflex over (x)}^(f)=[{circumflex over (x)}^(f1) . . . {circumflex over (x)}^(fn)]. Vector u^(f) thus contains the filtered inputs, the vector y^(f) the filtered outputs, and the vector {circumflex over (x)}^(f) the filtered auxiliary model outputs. The time constant for the filters is τ and this is used to calculate the coefficient matrix (A) and driving vector (B) as follows: $\begin{matrix} {{A = \begin{bmatrix} a_{11} & 0 & 0 \\ \vdots & ⋰ & 0 \\ a_{n\quad 1} & \ldots & a_{n\quad m} \end{bmatrix}};\quad {a_{ij} = {\gamma \left( {1 - \gamma} \right)}^{i - j}}} & (48) \\ {{B = \begin{bmatrix} b_{1} & \ldots & b_{n} \end{bmatrix}};{b_{i} = \left( {1 - \gamma} \right)^{i}};{\gamma = {\exp \left( \frac{{- \Delta}\quad t}{\tau} \right)}}} & (49) \end{matrix}$

[0081] where Δt is the sampling interval. Both the regressor vector φ and the instrumental variable vector η used in the parameter estimation can now be constructed directly from the outputs of the state-space models described above.

[0082] Referring to FIG. 2, the transformation 28 is performed to obtain the desired first-order plus time delay parameters from the second-order model parameters. The parameter estimation method outlined so far allows the parameters in a second-order transfer function model to be estimated for a given data set. The recursive implementation provides for a near-optimal least-squares fit; the sub-optimality only stemming from having to initialize the P matrix to some finite value. If P₀ is selected to be a large multiple of the identity matrix, this initialization effect can usually be considered negligible and a least-squares estimate of the parameters can be assumed.

[0083] As explained above, the ultimate objective of the testing tool 18 is to provide parameters for a first-order plus time delay model. Hence, a further transformation is used to obtain the desired parameters from those estimated for the second-order transfer function. As outlined previously, estimation of the parameters in a true first-order plus time delay model for a given data set is typically a non-linear problem, which would require iterative methods to locate a global minimum. A number of techniques for obtaining the time delay and time constant parameters from either a process response curve or a transfer function may be used. These techniques may obviate the need for performing non-linear optimization. However, use of the techniques to transform a given transfer function to the desired first-order plus time delay form still uses iteration to obtain parameter estimates.

[0084] A transformation as provided in an exemplary embodiment avoids the need for non-linear optimization or iteration by fitting the magnitude and phase of the desired first-order plus time delay model to the second-order transfer function at the estimated natural frequency (ω_(N)). Considering the second-order transfer function model: ${G(s)} = \frac{b}{s^{2} + {a_{1}s} + a_{2}}$

[0085] the natural frequency is thus: ω_(N)={square root}{square root over (a₂)}. At this frequency, the magnitude and phase are respectively given by: $\begin{matrix} {{{G\left( {j\quad \omega_{N}} \right)}} = \frac{b}{a_{1}\sqrt{a_{2}}}} & (50) \\ {{\angle \quad {G\left( {j\quad \omega_{N}} \right)}} = {- \frac{\pi}{2}}} & (51) \end{matrix}$

[0086] The desired first-order plus time delay model is defined as: $\begin{matrix} {{G_{M}(s)} = \frac{\exp \left( {- {Ls}} \right)}{{Ts} + 1}} & (52) \end{matrix}$

[0087] Because the magnitude of G_(M) is a function of the time constant and not the time delay, the time constant can be calculated by equating the magnitudes. Setting the static gain of the model G_(M) equal to that of the second-order model thus yields: $\begin{matrix} {\frac{b}{a_{1}\sqrt{a_{2}}} = {\left. \frac{b/a_{2}}{\sqrt{1 + {a_{2}T^{2}}}}\Rightarrow T \right. = \sqrt{\frac{a_{1}^{2}}{a_{2}^{2}} - \frac{1}{a_{2}}}}} & (53) \end{matrix}$

[0088] The phase of the time delay model is given by:

<G _(M2)(jω _(N))=−Lω_(N)−tan⁻¹(ω_(N) T)  (54)

[0089] Equating the phase to that of the second-order model at the natural frequency (i.e., π/2) and substituting the expression for T thus yields: $\begin{matrix} {L = \frac{{- {\tan^{- 1}\left( {{- \tau}\sqrt{a_{2}}} \right)}} - {\pi/2}}{- \sqrt{a_{2}}}} & (55) \end{matrix}$

[0090] To illustrate the accuracy of the proposed transformation, it may be compared with a known method of moments and also with a known method of matching points in the time domain step response. FIG. 3 shows the results from the comparison where the time delay is varied as a fraction of the time constant from zero to three. The transfer function used in the analysis is estimated from data generated from a first-order plus time delay model using the least-squares SVF estimation technique described previously. It can be observed that both the method of moments and time domain method deteriorate rapidly in accuracy when L/T gets large, e.g., above 0.5, whereas the proposed method produces accurate results throughout the considered range.

[0091] Note that L and T are only calculated when a real-valued solution is possible. The following constraints thus need to be satisfied to yield real values for L and T: $\begin{matrix} {\frac{a_{1}^{2}}{a_{2}^{2}} > {\frac{1}{a_{2}}\quad {AND}\quad a_{2}} > 0} & (56) \end{matrix}$

[0092] An estimate of the steady-state gain (K) is obtained from the estimated second-order transfer function simply by solving for s=0, i.e., $\begin{matrix} {\hat{K} = {{G(0)} = \frac{b_{1}}{a_{2}}}} & (57) \end{matrix}$

[0093] The estimate for the steady-state gain coupled with the estimates of L and T thus yield the familiar three-parameter model that forms the basis of numerous loop-tuning methods.

[0094] The estimates of L and T are evaluated for their viability (reference number 30 in FIG. 2) by ensuring the following constraints: $\begin{matrix} {0 < \frac{L}{T} \leq {5\quad {AND}\quad \Delta \quad t} < {L + T} < T_{0}} & (58) \end{matrix}$

[0095] where T₀ is the user-supplied estimate of the maximum possible time constant that is used as the time constant in the state variable filters and in the high-pass filters. The sampling interval is Δt and it is assumed that the ratio of time delay to time constant would not exceed 5 for all types of HVAC systems likely to be encountered in practice. The estimated time delay and time constant are passed through a low-pass filter and the filtered values of L and Tare used in the auxiliary model 22 as the first and second time constants (τ₁and τ₂).

[0096] For the tool 18, a simple convergence test is based on calculating the absolute change in a parameter between estimates. Convergence is then flagged when the change is below a threshold for a statistically significant number of estimates, e.g., $\begin{matrix} {{\frac{1}{N}{\sum\limits_{j = 1}^{N}{{\theta_{j} - \theta_{j - 1}}}}} < C} & (59) \end{matrix}$

[0097] where C is a threshold, N is preferably a statistically significant number of samples, and θ is a parameter being tested. The threshold may depend on the range of the parameter θ, input signal excitation, and the dynamics of the process 36 under investigation. The dynamics of the process 36 tend to have a particular relevance in the approach utilized by the tool 18 because step changes are applied as excitation and information relevant for the estimation is accumulated in the parameter estimation at a rate determined by the open loop dynamics of the process 36. If the tool 18 were used in a closed loop, the dynamics would likely be faster than the open loop plant due to the action of a controller. Parameters converge at a rate similar to or slower than that determined by the open loop dynamics of the process 36 when applying step test signals as excitation. The dynamics of the process can be normalized out of the convergence test by assuming the parameters converge at a rate governed by a first-order dynamic equation: $\begin{matrix} {{\tau \quad {\frac{{\theta (t)}}{t}}} = {\theta_{ss} - {\theta (t)}}} & (60) \end{matrix}$

[0098] where the subscript ss denotes steady-state value and τ is the overall time constant of the process. The equation provides a measure of the steady state error θ_(ss)−θ(t), which can thus be calculated from the rate of change of the parameter estimate multiplied by the time constant of the process. The estimate can be further normalized to eliminate variations due to different ranges in the tested parameter by dividing it by the current parameter estimate to yield a relative value. Assuming approximation of the derivative with discrete samples, the normalized steady-state error (SSE) can thus be stated as: $\begin{matrix} {{SSE} = {\frac{\tau}{\theta_{j}}{\frac{\theta_{j} - \theta_{j - 1}}{t_{j} - t_{j - 1}}}}} & (61) \end{matrix}$

[0099] Because the overall time constant of the process is not known a priori, one way to avoid having to specify this parameter would be to perform the convergence test on the estimate of this parameter. For example, if θ=T_(ar)=L+T, this is an estimate of the average residence time of the process and can also act as a proxy for the governing process time constant (τ). Hence, τ can be set equal to θ_(j) in Equation 61, yielding: $\begin{matrix} {{SSE} = {\frac{\theta_{j} - \theta_{j - 1}}{t_{j} - t_{j - 1}}}} & (62) \end{matrix}$

[0100] Statistical significance can again be tested for by evaluating the SSE quantity over a significant number of samples (N), i.e., $\begin{matrix} {{\frac{1}{N}{\sum\limits_{j = 1}^{N}\frac{{\theta_{j} - \theta_{j - 1}}}{t_{j} - t_{j - 1}}}} < C} & (63) \end{matrix}$

[0101] The threshold C can now be specified in generic terms between 0 and 1 related to the relative change in the estimate of the average residence time of the process under investigation. In this way, a threshold can be defined that is scalable across a range of different processes.

[0102] According to a preferred embodiment, the parameter estimation portion of the algorithm comprises two main parts: initialization and run-time.

[0103] In the initialization part, the algorithm is initialized through the construction of the following matrices and vectors: $\begin{matrix} {{A = \begin{bmatrix} a_{11} & 0 & 0 \\ \vdots & ⋰ & 0 \\ a_{n\quad 1} & \ldots & a_{n\quad m} \end{bmatrix}};\quad {a_{ij} = {\gamma \left( {1 - \gamma} \right)}^{i - j}}} & (64) \end{matrix}$

 B=[b ₁ . . . b _(n) ]; b ₁₌(1−y)¹  (65)

[0104] $\begin{matrix} {{M = \begin{bmatrix} m_{11} & 0 & 0 \\ \vdots & ⋰ & 0 \\ m_{n\quad 1} & \ldots & m_{n\quad n} \end{bmatrix}};\quad {m_{ij} = {\left( {- 1} \right)^{i - j}\begin{pmatrix} {n - j} \\ {i - j} \end{pmatrix}\tau^{j}}}} & (66) \\ {F = \begin{bmatrix} M & 0 \\ 0 & M \end{bmatrix}} & (67) \\ {{G = \begin{bmatrix} {g_{1}\quad \ldots \quad g_{n}} & {0\quad \ldots \quad 0} \end{bmatrix}};\quad {g_{i} = {\begin{pmatrix} n \\ i \end{pmatrix}\left( {- 1} \right)^{i}}}} & (68) \end{matrix}$

[0105] where γ=exp(−Δt/τ). The SVF time constant and high-pass filter time constant are both set equal to T₀, such that τ=T₀. The algorithm therefore preferably has the user to set two parameters: sampling interval (Δt) and initial time constant (T₀).

[0106] The run-time part of the algorithm comprises the following:

[0107] 1. Calculating matrices for auxiliary model: $\begin{matrix} {{C = \begin{bmatrix} \rho_{1} & 0 \\ \left\{ {\rho_{1}\left( {1 - \rho_{2}} \right)} \right\} & \rho_{2} \end{bmatrix}};\quad {d = \begin{bmatrix} \left( {1 - \rho_{1}} \right) & \left\{ {\left( {1 - \rho_{1}} \right)\left( {1 - \rho_{2}} \right)} \right\} \end{bmatrix}}} & (69) \end{matrix}$

[0108] 2. Applying high-pass filter to measured process output: $\begin{matrix} {y_{k}^{h\quad p} = {{\gamma \left( y_{k - 1}^{h\quad p} \right)} + {\gamma \left\lbrack {y_{k} - y_{k - 1}} \right\rbrack}}} & (70) \end{matrix}$

[0109] 3. Updating auxiliary model discrete state space:

{circumflex over (x)} _(k+1) =C{circumflex over (x)} _(k) +du _(k) ; {circumflex over (x)}=[{circumflex over (x)} ₁ {circumflex over (x)} ₂]  (71)

[0110] 4. Updating discrete state space of state variable filters: $\begin{matrix} \begin{matrix} {u_{k + 1}^{f} = {{A\quad u_{k}^{f}} + {B\quad u_{k}}}} \\ {y_{k + 1}^{f} = {{A\quad y_{k}^{f}} + {B\quad y_{k}^{h\quad p}}}} \\ {{\hat{x}}_{k + 1}^{f} = {{A\quad {\hat{x}}_{k}^{f}} + {B\quad {\hat{x}}_{2,k}^{h\quad p}}}} \end{matrix} & (72) \end{matrix}$

[0111] 5. Constructing regressor and instrumental variable vectors: $\begin{matrix} {\phi_{k}^{T} = \left\lfloor \begin{matrix} \left( y^{h\quad p} \right)_{k}^{f1} & {\left( y^{h\quad p} \right)_{k}^{f2}\left\{ {u_{k}^{f2} - u_{k}^{f3}} \right\}} \end{matrix} \right\rfloor} & (73) \\ {\eta_{k}^{T} = \left\lfloor \begin{matrix} \left( {\hat{x}}_{2}^{h\quad p} \right)_{k}^{f1} & {\left( {\hat{x}}_{2}^{h\quad p} \right)_{k}^{f2}\left\{ {u_{k}^{f2} - u_{k}^{f3}} \right\}} \end{matrix} \right\rfloor} & (74) \end{matrix}$

[0112] 6. Transforming regressor vector and calculating prediction error (e): $\begin{matrix} \begin{matrix} {{\psi_{i} = {F^{T}\phi_{i}}};} \\ {e_{k} = {y_{k}^{h\quad p} - \left( {{\theta_{k - 1}^{T}\psi_{k}} + {G^{T}\phi_{k}}} \right)}} \end{matrix} & (75) \end{matrix}$

[0113] 7. Updating P matrix: $\begin{matrix} {P_{k} = {P_{k - 1} - \frac{P_{k - 1}\eta_{k}\psi_{k}^{T}P_{k - 1}}{1 + {\psi_{k}^{T}P_{k - 1}\eta_{k}}}}} & (76) \end{matrix}$

[0114] 8. Updating second-order model parameter vector:

{circumflex over (θ)}_(k)={circumflex over (θ)}_(k−1) +P _(k)η_(k) e _(k); where θ^(T) =[−a ₁ −a ₂ b ₁]  (77)

[0115] 9. Calculating steady-state gain: $\begin{matrix} {\hat{K} = \frac{b_{1}}{a_{2}}} & (78) \end{matrix}$

[0116] 10. Calculating time constant (T) and time delay (L): $\begin{matrix} {\hat{T} = \sqrt{\frac{a_{1}^{2}}{a_{2}^{2}} - \frac{1}{a^{2}}}} & (79) \\ {\hat{L} = \frac{{- {\tan^{- 1}\left( {{- \tau}\sqrt{a_{2}}} \right)}} - {\pi/2}}{- \sqrt{a_{2}}}} & (80) \end{matrix}$

[0117] where ρ₁=exp (−Δt/τ₁) and τ₂=exp (−Δt/τ₂). The time constants τ₁ and τ₂ are updated with low-pass filtered versions of (viable) estimated L and T values. In the beginning, the two auxiliary model time constants are derived from T₀, such that: τ₁=T₀/2; τ₂=T₀/2. When convergence has occurred, the estimated L and T values are used to determine when to terminate the test or apply a new step test.

[0118] According to a preferred embodiment, an empirical analysis of the testing tool 18 is performed to evaluate the sensitivity of the algorithm to the initial estimate of the SVF time constant and the sampling rate at which data are obtained from the test system. In the analysis, the tool 18 was configured to terminate testing after applying two step-changes to the system input. Estimates for K, L, and T were obtained by taking the average of the estimates obtained from each step test, i.e., one step 0%-100% and one step 100%-0%.

[0119] According to a preferred embodiment, the time constant value used in the SVFs should be equal to or greater than the largest time constant in the process being identified. If the value used is too small, the estimator 38 will only “see” the high-frequency parts of the response and will not be able to correctly identify the system. The initial value of the SVF time constant is preferably equal to or greater than the largest time constant in the system. An empirical evaluation of the testing tool 18 can be used to investigate the sensitivity of the method to the initial time constant estimate, as described further below.

[0120] According to an exemplary embodiment, a first-order plus time delay system may be simulated and the ability of the testing tool 18 to estimate the system parameters for different initial time constant values may be investigated. According to an exemplary embodiment, the system is configured to have a time delay of 50 seconds, a time constant of 200 seconds, and a gain of 20° F. (e.g., parameter values representative of an HVAC heating coil unit). The initial SVF time constant was varied between 1 and 64 times the average residence time (L+T) of the simulated system. The testing tool 18 was set up to apply two step test signals (0-100% and 100%-0) and the results of the estimation represented by the average of the estimates at the end of each step test are shown in Table 1. It can be seen from the table that the final estimates are quite insensitive to the initial time constant value with the parameter estimates being close to the actual values, even when the SVF time constant is many orders of magnitude higher than that of the system. When the time constant estimate is too close to the actual value of the algorithm, it may not converge due to the parameters not passing the viability test. Recall that the viability test 30 specified that the sum of L and T should be below T₀. Because the algorithm tended to overestimate the time constants, the parameters did not pass this test. TABLE 1 Sensitivity of parameter estimates to initial SVF time constant Initial TC Time Constant Time Delay Static Gain estimate Parameter (secs) (secs) (deg F.) (secs) Actual Values 200.0 50.0 20.0 n/a Testing Tool Fail Fail Fail 250 Estimates 211.5 57.4 19.8 500 212.7 56.9 19.8 1000 212.9 56.9 19.8 2000 212.9 57.0 19.8 4000 213.3 56.8 19.9 8000 213.4 56.7 19.8 16000

[0121]FIG. 4 shows an example result from the test when the initial SVF time constant was equal to two times the actual average residence time. The top graph in each figure shows the measured output from the simulated system and the second graph shows the applied input that was generated by the testing tool. The third graph from the top shows the estimated static gain and the bottom graph shows the estimated time constant and time delay.

[0122] One of the advantages of the continuous-time parameter estimation method over discrete-time approaches is that the state variable filters 20 can be used for interpolating between the discretely measured inputs and outputs. The method may be less sensitive to the size of sample period and can even be used with data obtained at a variable sampling rate. To determine the sensitivity of the estimation to the sampling period, the tool 18 was tested empirically with the same simulation as in the previous section over a range of data sample periods (expressed as a fraction of the system average residence time). Table 2 shows results from tests where the sample period of the data presented to the algorithm was varied between 1 and {fraction (1/64)} the average residence time of 250 secs. In tests, the execution time step of the estimator itself was fixed at 1 second. In the test results, the method obtained estimates to a reasonable level of accuracy for sample periods that were less than ¼ the average residence time of the system. When tested at sampling intervals larger than ¼, the parameter estimates were either unreliable or the test failed to converge. The sampling rate should be fast enough to capture the dynamic characteristics of interest. In particular, the sampling period should be smaller in size than the time delay so that this parameter can be estimated. TABLE 2 Sensitivity of parameter estimates to sample period Parameter Time Constant (secs) Time Delay (secs) Static Gain (degF) $\begin{matrix} {{Ratio}\quad {of}\quad {sample}} \\ {{{period}\quad {to}\quad {Tar}},} \\ {{i.e.},\frac{\Delta t}{T + L}} \end{matrix}\quad$

Actual Values 200.0 50.0 20.0 N/a Testing Tool Fail Fail Fail 1 Estimates 119    0 11.8 1/2 215.6 47.2 19.9 1/4 212.2 49.7 19.8 1/8 213.3 59.7 19.8  1/16 212.6 59.0 19.9  1/32 212.9 55.5 19.8  1/64

[0123] The testing tool 18 utilizes an algorithm configured to identify a first-order plus time delay model from sampled input-output data. The algorithm is based on a state variable filter method that allows direct estimation of a second-order continuous-time transfer function. A transformation allows direct calculation of the parameters in the first-order plus time delay model from the second-order parameter values. The system and method is configured to provide near-optimal time delay and time constant estimates for a broad range of possible systems. Estimation of a three-parameter first-order plus time delay model is configured to make it possible to leverage numerous tuning methods for estimation of the parameters in PID controllers.

[0124] According to a preferred embodiment, the method is insensitive to the accuracy of initial user-defined configuration parameters: sampling interval and initial SVF time constant. The method is therefore expected to be more generically applicable than approaches based on discrete models where the sampling rate in particular can critically affect performance. The system and method could be used to characterize certain types of behavior or look for different types of faults. For example, the method could easily be used to determine static non-linearity by performing small steps through the control signal range and estimating the three parameters for each step. The variation in static gain (and also dynamic parameters) could then be used to determine non-linearity and assist in controller design, e.g., by determining a gain schedule.

[0125] The tool 18 is preferably used as a performance verification and tuning aide for HVAC systems. HVAC systems affect the comfort and health of building occupants and are responsible for a large portion of energy use in the developed world. Commissioning and performance verification of these systems is frequently inadequate due to time constraints and the manual nature of current practice. The tool 18 provides a basis on which to automate some of the testing in order to improve the commissioning process.

[0126] It is also important to note that the construction and arrangement of the elements of the system and method for an automated testing tool as shown in the preferred and other exemplary embodiments are illustrative only. Although only a few embodiments of the present invention have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in the claims. For example, although the proposed testing tool is implemented to generate a particular type of test signal to excite the process under investigation, the parameter estimation procedure is developed without any assumption of the type of test signal or excitation profile. The tool could therefore be adapted to operate with different types of test signal or also in a passive mode as part of an adaptive model-based control or fault detection scheme. Accordingly, all such modifications are intended to be included within the scope of the present invention as defined in the appended claims. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and/or omissions may be made in the design, operating conditions and arrangement of the preferred and other exemplary embodiments without departing from the spirit of the present invention as expressed in the appended claims. 

What is claimed is:
 1. A method of identifying a system, comprising: estimating at least one parameter in a second-order model of the system; and approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter.
 2. The method of claim 1 wherein the second-order model is a second-order continuous-time model.
 3. The method of claim 1 wherein the at least one second-order model parameter is a plurality of second-order continuous-time parameters.
 4. The method of claim 1 wherein the first-order model is a first-order plus time delay model.
 5. The method of claim 1 wherein the at least one first-order model parameter includes one or more of a time delay parameter, a time constant parameter and a static gain parameter.
 6. The method of claim 1 wherein the second-order model is a second-order transfer function model.
 7. The method of claim 1 wherein the at least one second-order model parameter is estimated using sampled inputs and outputs of the system.
 8. The method of claim 7 wherein the at least one second-order model parameter is estimated from the sampled inputs and outputs using a recursive least squares method.
 9. The method of claim 7 wherein the output of the system is a continuous signal that is discretely sampled.
 10. The method of claim 1 wherein the at least one first-order model parameter is approximated from the at least one second-order model parameter using a transformation function.
 11. The method of claim 10 wherein the transformation function is a non-linear transformation.
 12. The method of claim 1 wherein the at least one second-order model parameter is estimated using state variable filters.
 13. The method of claim 1 wherein the at least one first-order model parameter is used for at least one of pre-tuning a feedback controller, configuring the feedback controller, reconfiguring the feedback controller, evaluating performance of the system, and validating performance of the system.
 14. The method of claim 1 wherein the method is automated by a controller.
 15. The method of claim 1 wherein the system is an HVAC system.
 16. A method of analyzing a system to estimate at least one parameter in a first-order model of the system, the method comprising: sampling an input to the system; sampling an output from the system; estimating at least one parameter in a second-order model of the system using the sampled inputs and outputs; and approximating the at least one first-order model parameter based on the at least one second-order model parameter.
 17. The method of claim 16 wherein the second-order model is a second-order continuous-time model, and the at least one second-order model parameter is a plurality of second-order continuous-time parameters.
 19. The method of claim 16 wherein the at least one first-order model parameter comprises one or more of a time delay parameter, a time constant parameter and a static gain parameter.
 20. The method of claim 16 wherein the at least one second-order model parameter is estimated from the sampled inputs and outputs using a recursive least squares method.
 21. The method of claim 16 wherein the at least one first-order model parameter is approximated from the at least one second-order model parameter using a transformation function.
 22. The method of claim 21 wherein the transformation function is a non-linear transformation.
 23. The method of claim 16 wherein the at least one second-order model parameter is estimated using state variable filters.
 24. The method of claim 16 wherein the at least one first-order model parameter is used to perform at least one of pre-tuning a feedback controller, configuring the feedback controller, reconfiguring the feedback controller, evaluating performance of the system, and validating performance of the system.
 25. The method of claim 16 wherein the system is an HVAC system.
 26. The method of claim 16 wherein the method is automated by a controller.
 27. The method of claim 16, further comprising changing the input signal to the system.
 28. The method of claim 27, wherein the change in the input signal is a step change made for purposes of identifying the system.
 29. The method of claim 28 wherein the change in input signals is made during normal operation of the system.
 30. A method of analyzing a non-first order system having inputs and outputs, the method comprising: sampling the inputs; sampling the outputs; estimating at least one parameter for a first-order model of the system based on the sampled inputs and outputs.
 31. The method of claim 30 wherein the first-order model is a first-order plus time delay model.
 32. The method of claim 30 wherein the at least one first-order model parameter comprises one or more of a time delay parameter, a time constant parameter and a static gain parameter.
 33. The method of claim 30, further comprising generating a test signal.
 34. The method of claim 33 wherein the test signal is a step change in the inputs to the system.
 35. The method of claim 34, further comprising determining when to terminate the test or apply another step change to the inputs by applying a convergence test to the at least one first order model parameter.
 36. The method of claim 30 wherein the at least one first-order model parameter is estimated from at least one parameter of a second-order model of the system.
 37. The method of claim 36 wherein the second-order model is a second-order transfer function model and the at least one parameter thereof is estimated using state variable filters.
 38. The method of claim 37 wherein the at least one first-order model parameter is estimated from the at least one second-order model parameter using a transformation function.
 39. The method of claim 38 wherein the transformation function is a non-linear transformation.
 40. The method of claim 30 wherein the sampled outputs of the system are discrete measurements of a continuous output signal of the system.
 41. The method of claim 40, further comprising passing the sampled outputs through a filter.
 42. The method of claim 41 wherein the filter is a high-pass filter that focuses the estimation of at least one first-order parameter model on a frequency range of interest.
 43. The method of claim 30 further comprising passing the at least one first-order model parameter through a filter.
 44. The method of claim 43 wherein the filter is a low-pass filter that checks the viability of the first-order model.
 45. The method of claim 30 wherein the step of estimating the at least one first-order model parameter is implemented in a digital controller.
 46. The method of claim 30 wherein the non-first-order system is an HVAC system.
 47. An apparatus for identifying a system, comprising: means for estimating at least one parameter in a second-order model of the system; and means for approximating at least one parameter in a first-order model of the system from the at least one second-order model parameter.
 48. The apparatus of claim 47 wherein the second-order model is a second-order continuous-time model.
 49. The apparatus of claim 47 wherein the at least one second-order model parameter is a plurality of second-order continuous-time parameters.
 50. The apparatus of claim 47 wherein the first-order model is a first-order plus time delay model.
 51. The apparatus of claim 47 wherein the at least one first-order model parameter includes one or more of a time delay parameter, a time constant parameter and a static gain parameter.
 52. The apparatus of claim 47 wherein the second-order model is a second-order transfer function model.
 53. The apparatus of claim 47, further including means for sampling inputs and outputs of the system, and wherein the at least one second-order model parameter is estimated using the sampled inputs and outputs.
 54. The apparatus of claim 53 wherein the at least one second-order model parameter is estimated from the sampled inputs and outputs using a recursive least squares method.
 55. The apparatus of claim 53 wherein the output of the system is a continuous signal that is discretely sampled.
 56. The apparatus of claim 47 wherein the at least one first-order model parameter is approximated from the at least one second-order model parameter using a transformation function.
 57. The apparatus of claim 56 wherein the transformation function is a non-linear transformation.
 58. The apparatus of claim 47 wherein the at least one second-order model parameter is estimated using state variable filters.
 59. The apparatus of claim 47 wherein the at least one first-order model parameter is used for at least one of pre-tuning a feedback controller, configuring the feedback controller, reconfiguring the feedback controller, evaluating performance of the system, and validating performance of the system.
 60. The apparatus of claim 47, further including a controller running a program to perform the estimating means and the approximating means automatically.
 61. The apparatus of claim 47 wherein the system is an HVAC system. 